Determination method, determination apparatus, and recording medium

ABSTRACT

In a method of determining the type of each cell contained in a sample, one Raman spectrum is acquired from one undetermined cell, a plurality of degrees of matching of a Raman spectrum of the undetermined cell with respect to spectra of a plurality of principal components obtained by principal component analysis of a plurality of Raman spectra that are obtained one by one from each of a plurality of known types of cells are calculated, and a type of the undetermined cell is determined by classifying the plurality of degrees of matching based on a result obtained by classifying a plurality of principal component scores corresponding to each of the plurality of known types of cells obtained by the principal component analysis depending on the type of cells by a learning model using supervised learning.

CROSS REFERENCE TO RELATED APPLICATION

This Application is a 371 of PCT/JP2018/045591 filed on Dec. 12, 2018which, in turn, claimed the priority of Japanese Patent Application No.2017-238644 filed on Dec. 13, 2017, both applications are incorporatedherein by reference.

FIELD

The present invention relates to a determination method, a determinationapparatus, and a recording medium for determining a cell type based on aRaman spectrum.

BACKGROUND

In the field of regenerative medicine, it is necessary to examinewhether or not cultured cells have differentiated into desired types ofcells. In the field of medical diagnosis, it is sometimes necessary todetermine whether or not cells collected from a patient are normal typesof cells. Thus, a method for determining the type of each cell derivedfrom an organism is required. In the case of using a method of stainingcells or a method of destroying cells, it is not possible to observe thetime-series changes of determined cells or to culture the determinedcells. For this reason, it is desirable that the method of determiningthe cell type is a non-destructive and non-invasive method.

As such a method, there is a method using Raman spectroscopy. JapanesePatent Laid-Open Publication No. 2010-181391 describes that principalcomponent analysis is performed on a Raman spectrum measured from aplurality of locations of a cell or a Raman spectrum in a predeterminedwavelength range and the cell is determined from the obtained principalcomponent score.

SUMMARY

When measuring the Raman scattered light from a cell, the Ramanscattered light is generated from each portion in the cell, such as acell membrane, a nucleus, and a Golgi body. For this reason, the Ramanspectrum obtained from the cell has a complicated shape due to thesuperposition of a large number of signals. Therefore, it is difficultto find out a Raman band characterizing the cell from the Raman spectrumobtained from the cell and evaluate the Raman band. The techniquedescribed in Japanese Patent Laid-Open Publication No. 2010-181391 has aproblem that the time required for measurement is long since it isnecessary to obtain the distribution of the Raman spectrum in the cell.In addition, even in cells of the same type, the distribution of variousstructures within the cells is diverse. Accordingly, the importance ofobtaining the distribution of the Raman spectrum depending on thedistribution of the structures is not clear. In other words, a method ofaccurately determining the cell type using the Raman spectrum has notbeen established yet.

The present disclosure has been made in view of such circumstances, andit is an object to provide a determination method, a determinationapparatus, and a recording medium capable of determining a cell typebased on a Raman spectrum obtained from a cell more accurately thanbefore.

A determination method of determining a type of each cell contained in asample, according to an aspect of the present disclosure, comprises:acquiring one Raman spectrum from one undetermined cell; calculating aplurality of degrees of matching of a Raman spectrum of the undeterminedcell with respect to spectra of a plurality of principal componentsobtained by principal component analysis of a plurality of Raman spectrathat are obtained one by one from each of a plurality of known types ofcells; and determining a type of the undetermined cell by classifyingthe plurality of degrees of matching, based on a result obtained byclassifying a plurality of principal component scores corresponding toeach of the plurality of known types of cells obtained by the principalcomponent analysis depending on the type of cells by a learning modelusing supervised learning.

In the determination method according to an aspect of the presentdisclosure, the learning model is a support vector machine.

In the determination method according to an aspect of the presentdisclosure, the machine learning of the learning model is performedusing, as training data, the plurality of principal component scorescorresponding to each of the plurality of known types of cells and eachof the types of the plurality of cells.

In the determination method according to an aspect of the presentdisclosure, one entire cell is irradiated with excitation light, and aRaman spectrum is acquired by measuring Raman scattered light from theone entire cell.

A learning method of learning for determining a type of each cellcontained in a sample based on a Raman spectrum, according to an aspectof the present disclosure, comprises: acquiring spectra of a pluralityof principal components, which are obtained by principal componentanalysis of a plurality of Raman spectra that are obtained one by onefrom each of a plurality of known types of cells, and a plurality ofprincipal component scores corresponding to each of the plurality ofcells; performing machine learning of a learning model so that aplurality of sets of the plurality of principal component scores areable to be classified depending on the type of the plurality of cells bythe learning model using supervised learning with, as training data, theplurality of sets of the plurality of principal component scores andeach of the types of the plurality of cells; and storing the spectra ofthe plurality of principal components and a result of classification ofthe plurality of principal component scores by the learning model afterlearning.

In the learning method according to an aspect of the present disclosure,the plurality of sets of the plurality of principal component scores areclassified by dividing a coordinate space, in which a plurality ofcoordinate points having the plurality of principal component scores ascoordinate components are included, into a plurality of regions by thelearning model.

A determination apparatus for determining a type of each cell containedin a sample, according to an aspect of the present disclosure,comprises: a calculation unit that calculates a plurality of degrees ofmatching of a Raman spectrum acquired from an undetermined cell withrespect to spectra of a plurality of principal components obtained byprincipal component analysis of a plurality of Raman spectra that areobtained one by one from each of a plurality of known types of cells;and a determination unit that determines a type of the undetermined cellby classifying the plurality of degrees of matching based on a resultobtained by classifying a plurality of principal component scorescorresponding to each of the plurality of known types of cells obtainedby the principal component analysis depending on the type of cells by alearning model using supervised learning.

The determination apparatus according to an aspect of the presentdisclosure further comprises a learning unit that performs machinelearning of the learning model using, as training data, the plurality ofprincipal component scores corresponding to each of the plurality ofknown types of cells and each of the types of the plurality of cells.

The determination apparatus according to an aspect of the presentdisclosure further comprises a first acquisition unit that acquires thetraining data from outside.

The determination apparatus according to an aspect of the presentdisclosure further comprises a second acquisition unit that acquires,from outside, the spectra of the plurality of principal components and aresult, which is obtained by classifying the plurality of principalcomponent scores corresponding to each of the plurality of known typesof cells depending on the type of cells by the learning model.

A computer program for causing a computer to execute a process fordetermining a type of each cell contained in a sample, according to anaspect of the present disclosure, causes the computer to execute aprocess including: a step of calculating a plurality of degrees ofmatching indicating a degree of contribution of a Raman spectrumacquired from an undetermined cell to spectra of a plurality ofprincipal components obtained by principal component analysis of aplurality of Raman spectra that are obtained one by one from each of aplurality of known types of cells; and a step of determining a type ofthe undetermined cell by classifying the plurality of degrees ofmatching, based on a result obtained by classifying a plurality ofprincipal component scores corresponding to each of the plurality ofknown types of cells obtained by the principal component analysisdepending on the type of cells by a learning model using supervisedlearning.

A computer program for causing a computer to perform learning fordetermining a type of each cell contained in a sample, according to anaspect of the present disclosure, causes the computer to execute aprocess including: a step of acquiring spectra of a plurality ofprincipal components, which are obtained by principal component analysisof a plurality of Raman spectra that are obtained one by one from eachof a plurality of known types of cells, and a plurality of principalcomponent scores corresponding to each of the plurality of cells; a stepof performing machine learning of a learning model so that the pluralityof principal component scores are able to be classified depending on thetype of the plurality of cells by the learning model using supervisedlearning with, as training data, a plurality of sets of the plurality ofprincipal component scores and each of the types of the plurality ofcells; and a step of storing the spectra of the plurality of principalcomponents and a result of classification of the plurality of principalcomponent scores by the learning model after learning.

In an aspect of the present disclosure, one Raman spectrum is acquiredfrom one cell, and the type of the cell is determined based on theacquired Raman spectrum. In addition, based on the result of theprincipal component analysis of the plurality of Raman spectra obtainedfrom the plurality of known types of cells, a plurality of degrees ofmatching indicating the degree of matching of the Raman spectrum of theundetermined cell with respect to the spectrum of the principalcomponent are calculated. By classifying the plurality of degrees ofmatching corresponding to the undetermined cell based on the resultobtained by classifying the plurality of principal component scorescorresponding to the plurality of known types of cells by the learningmodel using supervised learning, the type of the cell is determined. Byusing the Raman spectrum obtained one by one from each cell, processingfor determination according to the characteristics of the entire cellcan be performed. In addition, the determination may be performed usingthe entire Raman spectrum, or the determination may be performed using apart of the Raman spectrum without using the other part. In addition,the learning may be performed using the entire Raman spectrum, or thelearning may be performed using a part of the Raman spectrum withoutusing the other part.

In addition, in an aspect of the present disclosure, by using thesupport vector machine as a learning model using supervised learning, aplurality of principal component scores corresponding to each of aplurality of cells can be easily classified, and a plurality of degreesof matching corresponding to the undetermined cell can be easilyclassified.

In an aspect of the present disclosure, machine learning of a learningmodel, such as a support vector machine, is performed using, as trainingdata, a plurality of principal component scores corresponding to each ofa plurality of known types of cells and the type of each cell. As aresult of machine learning, it is possible to determine the cell typemore accurately.

In addition, in an aspect of the present disclosure, the determinationapparatus for determining the cell type acquires the training data fromthe outside. The learning model can be learned based on the result ofthe Raman spectrum measurement performed by a person other than the userof the determination apparatus.

In addition, in an aspect of the present disclosure, the determinationapparatus acquires, from the outside, the spectra of the plurality ofprincipal components, which are obtained by principal component analysisof the plurality of Raman spectra obtained from the plurality of knowntypes of cells, and the result obtained by classifying the plurality ofprincipal component scores corresponding to each of the plurality ofknown types of cells according to types using a learning model. The celltype can be determined using the result of learning performed by aperson other than the user of the determination apparatus.

In addition, in an aspect of the present disclosure, when classifyingthe plurality of principal component scores corresponding to each of theplurality of known types of cells, the coordinate space includingcoordinate points having a plurality of principal component scores ascoordinate components is divided into a plurality of regions by alearning model, such as a support vector machine. For example, thecoordinate points are two-dimensional coordinate points having a firstprincipal component score and a second principal component score ascoordinate components. According to the division of the coordinatespace, the plurality of principal component scores are classified bycell types.

In addition, in an aspect of the present disclosure, when acquiring oneRaman spectrum from one cell, excitation light is emitted to one entirecell, and the Raman scattered light from the one entire cell ismeasured. The measurement of the Raman spectrum can be performed in ashort time, and the measurement of the Raman spectrum reflecting thecharacteristics of the entire cell can be performed.

In an aspect of the present disclosure, processing for determinationaccording to the characteristics of the entire cell is performed withoutbeing affected by the detailed structure in the cell, and the cell typeis determined by comparing the overall characteristics of the Ramanspectrum instead of using some Raman bands included in the Ramanspectrum. Therefore, an aspect of the present disclosure has anexcellent effect, for example, it is possible to determine the cell typemore accurately than before.

The above and further objects and features will more fully be apparentfrom the following detailed description with accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating the configuration of a Ramanscattered light measuring apparatus;

FIG. 2 is a block diagram illustrating the internal configuration of adetermination apparatus;

FIG. 3 is a flowchart illustrating the procedure of a process in which adetermination apparatus performs learning;

FIG. 4 is a graph showing an example of Raman spectra.

FIG. 5 is a table showing an example of Raman spectrum data;

FIG. 6A is a graph showing an example of the spectrum of each principalcomponent obtained by performing principal component analysis of aplurality of Raman spectra obtained from a plurality of RBLs and aplurality of CHOs;

FIG. 6B is a graph showing an example of the spectrum of each principalcomponent obtained by performing principal component analysis of aplurality of Raman spectra obtained from a plurality of RBLs and aplurality of CHOs;

FIG. 7 is a table showing an example of a result of calculation of aprincipal component score;

FIG. 8 is a characteristic diagram showing an example in which acoordinate space is divided by a support vector machine according to afirst embodiment;

FIG. 9 is a block diagram illustrating an example of the internalconfiguration of a storage device;

FIG. 10 is a flowchart illustrating the procedure of a process in whichtraining data is acquired from the outside and a determination apparatusperforms learning;

FIG. 11 is a flowchart illustrating the procedure of a process in whicha determination apparatus performs learning by acquiring learned datafrom the outside;

FIG. 12 is a flowchart illustrating the procedure of a process in whicha determination apparatus determines a cell type.

FIG. 13 is a graph showing an example of a fingerprint region in theRaman spectra;

FIG. 14A is a graph showing an example of the spectrum of a principalcomponent relevant to a fingerprint region;

FIG. 14B is a graph showing an example of the spectrum of a principalcomponent relevant to a fingerprint region; and

FIG. 15 is a characteristic diagram showing an example in which acoordinate space is divided by a support vector machine according to asecond embodiment.

DETAILED DESCRIPTION

Hereinafter, the present disclosure will be specifically described withreference to the diagrams showing the embodiments.

In a cell type determination method according to the present embodiment,a Raman scattered light measuring apparatus measures the Raman spectrumof each cell from a sample containing a plurality of cells, and adetermination apparatus determines the cell type based on the measuredRaman spectrum. The sample contains a plurality of types of cells. Forexample, the sample is a sample containing cultured cells or a samplecontaining cells collected from an organism, such as a human. Here, thedifference in cell type refers to a difference between an activated celland a non-activated cell, a difference between a live cell and a deadcell, a difference between a normal cell and an abnormal cell, and thelike.

First Embodiment

FIG. 1 is a block diagram illustrating the configuration of a Ramanscattered light measuring apparatus 1. The Raman scattered lightmeasuring apparatus 1 includes a sample holding unit 16 for holding asample 5, an irradiation unit 11 for emitting laser light, a mask 142, abeam splitter 141, and a lens 15. For example, the sample 5 is in aliquid state and stored in a container such as a petri dish. The sampleholding unit 16 is, for example, a sample table on which a containercontaining the sample 5 is placed. The sample holding unit 16 may be ina form other than the sample stage. The laser light emitted from theirradiation unit 11 is focused into thin light by the mask 142,reflected by the beam splitter 141, passes through the lens 15, and isirradiated to the sample 5. The irradiation unit 11, the mask 142, thebeam splitter 141, and the lens 15 are disposed so that the laser lightpasses only through the optical axis and the vicinity of the opticalaxis in the lens 15. In FIG. 1, the laser light is indicated by solidarrows.

The Raman scattered light measuring apparatus 1 further includes aspectroscope 13 and a detection unit 12 that detects light. Ramanscattering occurs in a portion of the sample 5 irradiated with the laserlight. The generated Raman scattered light is condensed by the lens 15,passes through the beam splitter 141, and is incident into thespectroscope 13. In FIG. 1, the Raman scattered light is indicated bybroken arrows. The spectroscope 13 disperses the incident Ramanscattered light. The detection unit 12 detects the light of eachwavelength that is dispersed by the spectroscope 13. The Raman scatteredlight measuring apparatus 1 includes an optical system including a largenumber of optical components such as a mirror, a lens, and a filter forguiding, condensing, and separating laser light and Raman scatteredlight. In FIG. 1, optical components other than the mask 142, the lens15, and the beam splitter 141 are omitted. In addition, the Ramanscattered light measuring apparatus 1 may have a configuration in whichthe laser light from the irradiation unit 11 passes through the beamsplitter 141 and the Raman scattered light is reflected by the beamsplitter 141.

The Raman scattered light measuring apparatus 1 further includes adriving unit 17 for moving the sample holding unit 16, a control unit18, and a camera 19 for observing the sample 5 held by the sampleholding unit 16. For example, the driving unit 17 moves the sampleholding unit 16 in the horizontal direction. Due to the movement of thesample holding unit 16 by the driving unit 17, the sample 5 moves, and aportion of the sample 5 irradiated with the laser light from theirradiation unit 11 is changed. That is, by the operation of the drivingunit 17, a portion where Raman scattered light is generated in thesample 5 is changed. The camera 19 includes, for example, an imagingdevice. The irradiation unit 11, the detection unit 12, the spectroscope13, the driving unit 17, and the camera 19 are connected to the controlunit 18.

The control unit 18 controls each unit of the Raman scattered lightmeasuring apparatus 1. The irradiation unit 11 is controlled on and offby the control unit 18. The wavelength of light detected by thedetection unit 12 after being dispersed by the spectroscope 13 iscontrolled by the control unit 18. The detection unit 12 outputs asignal corresponding to the detection intensity of the light of eachwavelength to the control unit 18. The control unit 18 receives thesignal output from the detection unit 12, and generates a Raman spectrumbased on the wavelength of the light dispersed by the spectroscope 13and the detection intensity of the light indicated by the input signal.The control unit 18 controls the operation of the driving unit 17 tomove the sample holding unit 16 and change a portion where Ramanscattered light is generated in the sample 5. In addition, the controlunit 18 causes the camera 19 to take an image of the sample 5, so that aportion where Raman scattered light is generated in the sample 5 can bechecked. In this manner, the Raman scattered light measuring apparatus 1can acquire the Raman spectrum from each portion of the sample 5. Forexample, the camera 19 is disposed so as to take an image of the sample5 using light on the same axis as the laser light emitted from theirradiation unit 11 to the sample 5. By performing taking the imageusing the light on the same axis as the laser light, it becomes easy tocheck and adjust the position of a portion where Raman scattered lightis generated in the sample 5.

A determination apparatus 2 that performs processing for determining thetype of each cell contained in the sample 5 is connected to the Ramanscattered light measuring apparatus 1. The determination apparatus 2 isconnected to the control unit 18. FIG. 2 is a block diagram illustratingthe internal configuration of the determination apparatus 2. Thedetermination apparatus 2 is configured using a computer, such as apersonal computer. The determination apparatus 2 includes a centralprocessing unit (CPU) 21 for performing calculation, a random accessmemory (RAM) 22 for storing temporary data generated by the calculation,a drive unit 23 for reading information from a recording medium 20 suchas an optical disk, and a non-volatile storage unit 24 such as a harddisk. In addition, the determination apparatus 2 includes an operationunit 25 such as a keyboard or a mouse for receiving a user's operation,a display unit 26 such as a liquid crystal display, an interface unit27, and a communication unit 28. The control unit 18 is connected to theinterface unit 27. The communication unit 28 is connected to acommunication network 3 outside the determination apparatus 2, andcommunicates with an external apparatus through the communicationnetwork 3. The communication unit 28 can download data from the outsidethrough the communication network 3.

The CPU 21 causes the drive unit 23 to read a computer program 241recorded on the recording medium 20, and stores the read computerprogram 241 in the storage unit 24. The CPU 21 loads the computerprogram 241 from the storage unit 24 to the RAM 22 as necessary, andperforms processing necessary for the determination apparatus 2according to the loaded computer program 241. In addition, the computerprogram 241 may be downloaded from the outside of the determinationapparatus 2 through the communication unit 28. The control unit 18outputs Raman spectrum data representing the generated Raman spectrum tothe determination apparatus 2. The determination apparatus 2 receivesthe Raman spectrum data at the interface unit 27, and the CPU 21 storesthe Raman spectrum data in the storage unit 24.

The Raman scattered light measuring apparatus 1 and the determinationapparatus 2 configure one analyzer. The determination apparatus 2 canalso control the operation of the Raman scattered light measuringapparatus 1. The control unit 18 may output image data representing animage of the sample 5 captured by using the camera 19 to thedetermination apparatus 2, the determination apparatus 2 may receive theimage data at the interface unit 27, and the CPU 21 may display theimage of the sample 5 represented by the image data on the display unit26. The user may operate the operation unit 25 to input an instructionfor controlling the Raman scattered light measuring apparatus 1. Forexample, the CPU 21 outputs a control signal, which is for giving aninstruction to measure the Raman spectrum of a specific portion in thesample 5, from the interface unit 27 to the control unit 18 according tothe input instruction. The control unit 18 receives the control signal,controls the operations of the irradiation unit 11, the detection unit12, the spectroscope 13, and the driving unit 17 according to thereceived control signal, and measures the Raman spectrum of a specificportion in the sample 5.

In the present embodiment, the determination apparatus 2 determines acell type by a support vector machine that is a learning model usingsupervised learning. In order to appropriately determine the cell type,it is necessary to perform the learning of the support vector machine.FIG. 3 is a flowchart illustrating the procedure of a process in whichthe determination apparatus 2 performs learning. The CPU 21 performsprocessing necessary for the determination apparatus 2 according to thecomputer program 241. The Raman scattered light measuring apparatus 1acquires a plurality of Raman spectra from the sample 5 containing aplurality of known types of cells (S11). The Raman scattered lightmeasuring apparatus 1 measures the Raman spectra one by one from each ofthe plurality of cells contained in the sample 5. For example, anoptical system including the lens 15 is adjusted so that the laser lightfrom the irradiation unit 11 is emitted to one entire cell, and theRaman scattered light measuring apparatus 1 irradiates the one entirecell with the laser light. The laser light passes through only theoptical axis and the vicinity of the optical axis in the lens 15 to beemitted to the one entire cell. The lens 15 collects Raman scatteredlight generated from the entire cell by irradiation with the laserlight, and the detection unit 12 detects Raman scattered light from theentire cell. An average Raman spectrum is obtained for one cell, and theobtained Raman spectrum reflects the characteristics of the entire cell.Since one Raman spectrum is acquired from one cell, the measurement timeis shortened as compared with a case where a plurality of Raman spectraare acquired from one cell.

The Raman scattered light measuring apparatus 1 acquires the Ramanspectra one by one from each of the plurality of cells while changingcells irradiated with the laser light, by moving the sample holding unit16 with the driving unit 17. For example, the control unit 18 detectseach cell using the camera 19, and controls the driving unit 17 so thatthe laser light is sequentially emitted to each cell. In addition, forexample, the user who has sighted the image of the sample 5 displayed onthe display unit 26 may operate the operation unit 25 to input aninstruction to change the position of the sample 5 to the determinationapparatus 2. A control signal according to the instruction is input fromthe determination apparatus 2 to the control unit 18, and the controlunit 18 controls the driving unit 17 according to the control signal sothat the laser light is sequentially emitted to each cell. In thismanner, a plurality of Raman spectra are acquired from the sample 5.

FIG. 4 is a graph showing an example of Raman spectra. The horizontalaxis indicates a Raman shift, and the vertical axis indicates theintensity of Raman scattered light at each Raman shift. The Raman shiftis expressed by wave number, and the unit is cm⁻¹. The unit of theintensity of the Raman scattered light is arbitrary unit (a. u.). FIG. 4shows an example in which a rat basophilic leukemia cell (RBL) and aChinese hamster ovary cell (CHO) are used as cells. FIG. 4 shows twotypes of Raman spectra, with the baseline of the Raman spectrum of theCHO being a value larger than the baseline of the RBL. The shapes of thetwo types of Raman spectra are similar, and it is difficult todistinguish the two types of cells from the shapes of the Raman spectra.

The control unit 18 outputs Raman spectrum data indicating a pluralityof Raman spectra, and the determination apparatus 2 receives the Ramanspectrum data at the interface unit 27 and stores the Raman spectrumdata in the storage unit 24. The Raman spectrum data includes datarepresenting the Raman spectrum obtained from each of the plurality ofcells. The data indicating one Raman spectrum is associated with onecell in which the measured Raman scattered light is generated, that is,a cell from which the Raman spectrum is generated.

FIG. 5 is a table showing an example of Raman spectrum data. FIG. 5shows an example in which the number of cells for which the Ramanspectrum is measured is N (N is a natural number). In FIG. 5, respectivecells are numbered so as to be distinguished from each other, and thecell numbers are arranged in the vertical direction. In the Ramanspectrum data, the intensity value of the Raman scattered light measuredfor each Raman shift is recorded in association with each cell. FIG. 5includes ** indicating a numerical value. For each cell, a plurality ofthe intensity values of the Raman scattered light are arranged in thehorizontal direction, and each intensity value of the Raman scatteredlight corresponds to each Raman shift value. In FIG. 5, data indicatingthe Raman spectrum of the cell with the number 1 is surrounded by athick frame. The Raman spectrum of one cell is expressed bymultidimensional data including the intensity of Raman scattered lightfor each Raman shift. Raman spectrum data includes a plurality of piecesof multidimensional data corresponding to a plurality of cells.

Then, the CPU 21 performs background processing for removing abackground signal from the plurality of acquired Raman spectra (S12).Then, the CPU 21 performs principal component analysis of the pluralityof Raman spectra (S13). In S13, the CPU 21 performs principal componentanalysis of the plurality of pieces of multidimensional datarepresenting a plurality of Raman spectra. Specifically, assuming thatthe number of cells for which the Raman spectrum is measured is N andthe number of intensity values of Raman scattered light included in oneRaman spectrum is M (M is a natural number), principal componentanalysis is performed on a matrix of N rows and M columns havingintensity values of Raman scattered light as elements. For example, amatrix surrounded by the frame shown in FIG. 5 is a target of theprincipal component analysis. Multidimensional data including M elementsin one row represent one Raman spectrum, and corresponds to one cell.

The CPU 21 performs calculation for generating spectra of a plurality ofprincipal components, such as a spectrum of the first principalcomponent in which information of the largest ratio among all pieces ofinformation of a plurality of Raman spectra is gathered and a spectrumof the second principal component in which information of the nextlargest ratio is gathered, by principal component analysis. FIGS. 6A and6B are graphs showing examples of the spectrum of each principalcomponent obtained by performing principal component analysis of aplurality of Raman spectra obtained from a plurality of RBLs and aplurality of CHOs. FIGS. 6A and 6B show the results of principalcomponent analysis of a plurality of Raman spectra obtained from aplurality of RBLs and a plurality of CHOs. FIG. 6A shows an example ofthe spectrum of the first principal component, and FIG. 6B shows anexample of the spectrum of the second principal component. Thehorizontal axis indicates a Raman shift, and the vertical axis indicatesthe intensity of Raman scattered light at each Raman shift.

In S13, the CPU 21 further calculates a plurality of principal componentscores for each of the plurality of Raman spectra. The principalcomponent score is a numerical value indicating the contribution ratioof each Raman spectrum to the spectrum of the principal component. Thefirst principal component score indicates the contribution ratio of oneRaman spectrum to the spectrum of the first principal component, and thesecond principal component score indicates the contribution ratio of oneRaman spectrum to the spectrum of the second principal component. Inother words, the principal component score indicates at which ratio therespective Raman spectra are combined to form the spectrum of theprincipal component. By adding a plurality of Raman spectra eachmultiplied by the first principal component score, a spectrum of thefirst principal component is obtained. The CPU 21 calculates a pluralityof principal component scores by matrix calculation. A plurality ofprincipal component scores are calculated for each Raman spectrum. As aresult, a plurality of principal component scores are obtained for eachRaman spectrum and cell.

FIG. 7 is a table showing an example of a result of calculation of aprincipal component score. A plurality of principal component scores arerecorded in association with the numbers of cells from which the Ramanspectra are generated. FIG. 7 includes ** in indicating a numericalvalue. A plurality of principal component scores are arranged in thehorizontal direction for each cell. In FIG. 7, values of a plurality ofprincipal component scores for a cell with the number 1 are surroundedby a thick frame. A set of a plurality of principal component scorescorresponds to one Raman spectrum, and corresponds to one cell fromwhich the Raman spectrum is generated. A plurality of sets of aplurality of principal component scores are calculated for a pluralityof Raman spectra and a plurality of cells. The CPU 21 stores theplurality of sets of the plurality of principal component scores in theRAM 22 or the storage unit 24.

Then, the CPU 21 generates a coordinate space including coordinatepoints having a plurality of principal component scores corresponding toeach of a plurality of cells as coordinate components (S14). Forexample, the CPU 21 generates a coordinate space including a pluralityof coordinate points corresponding to a plurality of cells by plotting,on the two-dimensional coordinates, a plurality of coordinate pointshaving a first principal component score and a second principalcomponent score as coordinate components. Cell information, whichindicates the type of cell for which the Raman spectrum has beenmeasured, is received through the interface unit 27 or the operationunit 25, and then the CPU 21 performs learning of the support vectormachine using the received cell information and the plurality ofprincipal component scores for the plurality of cells as training data(S15). In S15, the CPU 21 performs machine learning for adjusting theparameters of the support vector machine so that the plurality ofprincipal component scores for the respective cells can be classifiedaccording to the type of each cell indicated by the cell information.For example, in order to determine the cell type, the CPU 21 performsprocessing for dividing the coordinate space, which includes a pluralityof coordinate points having a first principal component score and asecond principal component score as coordinate components, into aplurality of regions using a support vector machine. At this time, theCPU 21 adjusts the parameters of the support vector machine so as todivide the coordinate space so that coordinate points corresponding todifferent types of cells are included in different regions.

FIG. 8 is a characteristic diagram showing an example in which thecoordinate space is divided by the support vector machine according tothe first embodiment. In the diagram, the horizontal axis indicates thefirst principal component score, and the vertical axis indicates thesecond principal component score. A plurality of coordinate pointshaving a first principal component score and a second principalcomponent score as coordinate components are included in thetwo-dimensional coordinate space. FIG. 8 shows a plurality of coordinatepoints corresponding to a plurality of Raman spectra obtained from aplurality of RBLs and a plurality of coordinate points corresponding toa plurality of Raman spectra obtained from a plurality of CHOs. From thesame type of cells, Raman spectra having similar shapes are obtained,and the principal component scores are similar numerical values. Forthis reason, in the coordinate space, coordinate points corresponding tothe same type of cells tend to be located close to each other, andcoordinate points corresponding to different types of cells tend to belocated away from each other. A plurality of coordinate pointssurrounded by the broken line correspond to a plurality of CHOs. Aplurality of coordinate points surrounded by the one-dot chain linecorrespond to a plurality of RBLs. The CPU 21 adjusts the parameters ofthe support vector machine so that the dimensional coordinate space canbe divided into a region including two-coordinate points correspondingto CHOs and another region including coordinate points corresponding toRBLs by the support vector machine. In FIG. 8, a boundary 61 of theplurality of divided regions is indicated by the straight line. Inaddition, the CPU 21 adjusts the parameters of the support vectormachine so that the margin between the coordinate point and the boundary61 in the coordinate space is as large as possible. The processing ofS15 corresponds to a learning unit.

Then, the CPU 21 performs calculation for generating the boundary 61 bydividing the coordinate space so that the coordinate pointscorresponding to different types of cells are included in differentregions by the processing of the support vector machine after learning(S16). When dividing the two-dimensional coordinate space as shown inFIG. 8, the boundary 61 is generated as a straight line or a curve inthe two-dimensional space. Then, the CPU 21 stores the learned data,which indicates the learning result of the support vector machine andincludes data representing the spectra of the principal componentgenerated in S13, the boundary 61 generated in S16, and the type of eachcell corresponding to each region divided by the boundary 61, in thestorage unit 24 (S17). The boundary 61 and the type of each cellcorresponding to each region divided by the boundary 61 correspond to aresult obtained by classifying a plurality of principal component scorescorresponding to each of a plurality of known types of cells accordingto their types. As described above, the process in which thedetermination apparatus 2 performs learning is completed. The processingof S11 to S17 is repeated as necessary. For example, the types of cellscontained in the sample 5 are changed to repeat the processing of S11 toS17. For example, the processing of S11 to S17 is performed for acombination of a plurality of types that are assumed.

In the above description, an example in which the determinationapparatus 2 creates training data is shown. However, the determinationapparatus 2 may perform processing for acquiring the training data fromthe outside. A storage device 4 for storing training data is connectedto the communication network 3.

FIG. 9 is a block diagram illustrating an example of the internalconfiguration of the storage device 4. The storage device 4 is acomputer, such as a server apparatus. The storage device 4 includes aCPU 41, a RAM 42, a non-volatile storage unit 43 such as a hard disk,and a communication unit 44. The communication unit 44 is connected tothe communication network 3. The storage unit 43 stores a computerprogram 431. The CPU 41 performs various processes according to thecomputer program 431. In addition, the storage unit 43 stores trainingdata. The training data is uploaded to the storage device 4 by the makerof the Raman scattered light measuring apparatus 1 or the determinationapparatus 2 or by other users. In the present embodiment, an example inwhich the storage device 4 is configured by a single computer is shown.However, the storage device 4 may be configured by a plurality ofcomputers connected to each other through the communication network 3.

FIG. 10 is a flowchart illustrating the procedure of a process in whichtraining data is acquired from the outside and the determinationapparatus 2 performs learning. The determination apparatus 2 and thestorage device 4 perform authentication processing required for thedetermination apparatus 2 to acquire the training data from the storagedevice 4 (S21). For example, the CPU 21 of the determination apparatus 2causes the communication unit 28 to transmit authentication information,such as a password input by the user operating the operation unit 25, tothe storage device 4 through the communication network 3. The CPU 41 ofthe storage device 4 determines whether or not the authenticationinformation transmitted from the determination apparatus 2 is valid, andperforms processing for allowing the download of the training data ifthe authentication information is valid and disallowing the download ifthe authentication information is not valid. In addition, for example,the CPU 21 of the determination apparatus 2 performs processing forpaying a fee for using the training data. The CPU 41 of the storagedevice 4 performs processing for checking whether or not the processingfor payment has been performed, and performs processing for allowing thedownload of the training data when it is checked that the processing forpayment has been performed and disallowing the download when it is notchecked that the processing for payment has been performed. For example,the CPU 41 causes the communication unit 44 to transmit informationindicating the authentication result to the determination apparatus 2through the communication network 3.

When the authentication result that allows the download is obtained, theCPU 21 downloads the training data from the storage device 4 through thecommunication unit 28 and stores the data in the storage unit 24 (S22).For example, the CPU 21 performs the processing of S22 when theinformation indicating the authentication result allowing the downloadis received through the communication unit 28. The training dataincludes spectra of a plurality of principal components obtained byprincipal component analysis of Raman spectra of a plurality of cells, aplurality of principal component scores corresponding to each of theplurality of cells, and cell information indicating the type of eachcell. The processing of S22 corresponds to a first acquisition unit. TheCPU 21 performs machine learning of the support vector machine using thedownloaded training data (S23). The processing of S23 is the sameprocessing as the processing of S14 to S16. Then, the CPU 21 stores thelearned data, which includes data representing the spectra of theprincipal component included in the training data, the generatedboundary 61, and the type of each cell corresponding to each region, inthe storage unit 24 (S24), and ends the process in which thedetermination apparatus 2 performs learning.

In addition, the determination apparatus 2 may perform processing foracquiring Raman spectrum data from the outside. The storage device 4stores Raman spectrum data and cell information in the storage unit 43.The Raman spectrum data and the cell information are uploaded to thestorage device 4 by the maker of the Raman scattered light measuringapparatus 1 or the determination apparatus 2 or by other users. The CPU21 acquires the Raman spectrum data from the outside by downloading theRaman spectrum data and the cell information from the storage device 4through the communication network 3 by the communication unit 28 in S11,and stores the Raman spectrum data and the cell information in thestorage unit 24. The CPU 21 performs the processing of S12 to S17 usingthe acquired Raman spectrum data and cell information. Before performingthe download, the CPU 21 may perform the same authentication processingas in S21.

In addition, the training data used for learning may be data obtainedfrom a plurality of measurements. For example, the Raman scattered lightmeasuring apparatus 1 may perform measurement multiple times using aplurality of samples 5, and the determination apparatus 2 may createtraining data from the Raman spectra obtained by the plurality ofmeasurements. For example, the Raman scattered light measuring apparatus1 may perform measurement multiple times using a plurality of samples 5containing different cells, and the determination apparatus 2 may createtraining data from the Raman spectra obtained by the plurality ofmeasurements. In addition, the training data used for learning may bedata including both training data created from Raman spectrum data andtraining data acquired from the outside.

In addition, the determination apparatus 2 may perform processing foracquiring learned data from the outside. The storage device 4 stores, inthe storage unit 43, the learned data including data representing thespectra of the principal component, the boundary 61 generated by thesupport vector machine, and the type of each cell corresponding to eachregion divided by the boundary 61. The learned data is uploaded to thestorage device 4 by the maker of the Raman scattered light measuringapparatus 1 or the determination apparatus 2 or by other users. FIG. 11is a flowchart illustrating the procedure of a process in which thedetermination apparatus 2 performs learning by acquiring learned datafrom the outside. The determination apparatus 2 and the storage device 4perform authentication processing required for the determinationapparatus 2 to acquire the learned data from the storage device 4 (S31).For example, the CPU 21 of the determination apparatus 2 causes thecommunication unit 28 to transmit the authentication information to thestorage device 4, and the CPU 41 of the storage device 4 determineswhether or not the authentication information is valid, and performsprocessing for allowing the download of the learned data when theauthentication information is valid and disallowing the download whenthe authentication information is not valid. In addition, for example,the CPU 21 of the determination apparatus 2 performs processing forpaying a fee for using the learned data. The CPU 41 of the storagedevice 4 performs processing for allowing the download of the learneddata when it is checked that the processing for payment has beenperformed and disallowing the download when it is not checked that theprocessing for payment has been performed. For example, the CPU 41causes the communication unit 44 to transmit information indicating theauthentication result to the determination apparatus 2 through thecommunication network 3.

When the authentication result that allows the download is obtained, theCPU 21 downloads the learned data from the storage device 4 through thecommunication network 3 by the communication unit 28 (S32). For example,the CPU 21 performs the processing of S32 when the informationindicating the authentication result allowing the download is receivedthrough the communication unit 28. The process in S32 corresponds to asecond acquisition unit. Then, the CPU 21 stores the downloaded learneddata in the storage unit 24 (S33), and ends the process in which thedetermination apparatus 2 performs learning. In addition, thedetermination apparatus 2 may perform processing for uploading Ramanspectrum data, training data, or learned data to the storage device 4.

In the present embodiment, the determination apparatus 2 determines thetype of each cell contained in the sample 5 using the learned data. FIG.12 is a flowchart illustrating the procedure of a process in which thedetermination apparatus 2 determines a cell type. The CPU 21 performsprocessing necessary for the determination apparatus 2 according to thecomputer program 241. The Raman scattered light measuring apparatus 1acquires a Raman spectrum from the sample 5 containing one or more cellswhose types have not been determined (S41). The Raman scattered lightmeasuring apparatus 1 measures one Raman spectrum for each cellcontained in the sample 5 as in the measurement in S11. One Ramanspectrum is obtained when the number of cells to be determined is one,and a plurality of Raman spectra are obtained when the number of cellsto be determined is plural. The control unit 18 outputs Raman spectrumdata representing one or more Raman spectra, and the determinationapparatus 2 receives the Raman spectrum data at the interface unit 27and stores the Raman spectrum data in the storage unit 24.

Then, the CPU 21 performs background processing for removing abackground signal from the acquired Raman spectra (S42). Then, the CPU21 calculates a plurality of degrees of matching of the acquired Ramanspectra with respect to the spectra of a plurality of principalcomponents represented by the data included in the learned data (S43).The degree of matching is calculated by a method similar to that for theprincipal component score calculated in the principal componentanalysis. In S43, the CPU 21 calculates the degree of matching of theRaman spectrum of the cell whose type has not been determined withrespect to the spectra of a plurality of principal components using thesame calculation method as the calculation method for calculating aplurality of principal component scores for each of the plurality ofRaman spectra in S12. The CPU 21 calculates, for one Raman spectrum, thesame number of degrees of matching as the number of principal componentscores calculated in S12. For example, the CPU 21 calculates a pluralityof degrees of matching by performing a matrix calculation using a matrixused for calculating the principal component score in the matrixcalculation in S12. For example, the CPU 21 calculates a first degree ofmatching, which is calculated by a calculation method similar to thatfor the first principal component score, and a second degree ofmatching, which is calculated by a calculation method similar to thatfor the second principal component score. The first degree of matchingindicates how much the Raman spectrum of a cell whose type has not beendetermined matches the spectrum of the first principal componentrepresented by the learned data, and the second degree of matchingindicates how much the Raman spectrum matches the spectrum of the secondprincipal component. The processing of S43 corresponds to a calculationunit.

Then, the CPU 21 plots, in the coordinate space, coordinate pointshaving a plurality of degrees of matching corresponding to respectivecells as coordinate components (S44). For example, the CPU 21 plots, onthe two-dimensional coordinates, coordinate points having the firstdegree of matching and the second degree of matching as coordinatecomponents. Then, the CPU 21 divides the coordinate space into aplurality of regions by the boundary 61 represented by the data includedin the learned data, and determines in which region the coordinate pointcorresponding to each cell is included (S45).

Then, the CPU 21 determines the type of each cell corresponding to eachregion divided by the boundary 61 represented by the data included inthe learned data as the type of each cell corresponding to thecoordinate point determined to be included in each region, therebydetermining the type of the cell (S46). The processing of S44 to S46corresponds to a determination unit. Then, the CPU 21 stores dataindicating the result of determining the cell type in the storage unit24 (S47). The CPU 21 may display the result of determining the cell typeon the display unit 26. As described above, the processing fordetermining the cell type is completed.

For example, the determination apparatus 2 is used to determine cellsdifferentiated into a desired type by culturing and undifferentiatedcells. Training data is created based on the Raman spectra obtained fromcells determined to be differentiated and cells determined to beundifferentiated, and learning of the support vector machine isperformed to create learned data. Thereafter, the Raman scattered lightmeasuring apparatus 1 measures the Raman spectrum from the sample 5containing the cultured cells, and the determination apparatus 2determines whether each cell contained in the sample 5 is a celldifferentiated into a desired type or an undifferentiated cell based onthe learned data. When the type of each cell is determined by thesupport vector machine, the determination apparatus 2 can also determinethe degree of cell differentiation depending on the distance between theboundary 61 and the coordinate point corresponding to each cell in thecoordinate space.

For example, the determination apparatus 2 is used to determine whetheror not the collected cells are normal types of cells. Training data iscreated based on the Raman spectra obtained from cells determined to benormal and cells determined to be abnormal, and learning of the supportvector machine is performed to create learned data. Thereafter, theRaman scattered light measuring apparatus 1 measures the Raman spectrumfrom the sample 5 containing the collected cells, and the determinationapparatus 2 determines whether or not each cell contained in the sample5 is a normal cell based on the learned data. When the cell type isdetermined by the support vector machine, the determination apparatus 2can also determine the degree of abnormality of the abnormal celldepending on the distance between the boundary 61 and the coordinatepoint corresponding to each cell in the coordinate space.

As described in detail above, in the present embodiment, one Ramanspectrum is acquired from one cell, and the type of the cell isdetermined based on the acquired Raman spectrum. The time required forthe determination is shortened as compared with a method of determiningthe cell type using the distribution of the Raman spectrum in the cell.By using the Raman spectra obtained one by one from each cell,processing for determination based on the characteristics of the entirecell can be performed without being affected by the detailed structurein the cell, compared with a case of using the distribution of Ramanspectra in the cell.

In addition, in the present embodiment, the determination apparatus 2performs principal component analysis of a plurality of Raman spectraobtained from a plurality of known types of cells, and classifies aplurality of principal component scores corresponding to each of theplurality of Raman spectra based on their types using the support vectormachine. In addition, the determination apparatus 2 determines the celltype by classifying a plurality of degrees of matching corresponding toundetermined cells based on the classification result of the pluralityof principal component scores corresponding to the plurality of knowntypes of cells. From the same type of cells, Raman spectra havingsimilar shapes tend to be obtained, and the principal component scorestend to be similar numerical values. Therefore, since a set of principalcomponent scores corresponding to known types of cells can beclassified, each cell is determined by classifying the degrees ofmatching corresponding to undetermined cells based on the classificationresult. Instead of focusing on some Raman bands included in the Ramanspectrum as being characteristic of cells, the cell type is determinedby comparing the overall characteristics of the Raman spectrum.Therefore, it is possible to determine the cell type more accurately andeasily than before. In addition, the determination is performed usingthe support vector machine. Therefore, by performing the learning of thesupport vector machine, it is possible to improve the determinationapparatus 2 so that the cell type can be determined more accurately.

Second Embodiment

In a second embodiment, a form in which cell determination is performedusing a part of the Raman spectrum is shown. The Raman spectrum of acell includes a portion, which reflects the characteristics of the cellrelatively strongly and in which a change depending on the cell type islarge, and includes a portion, which does not reflect thecharacteristics of the cell much and in which a change depending on thecell type is small. Hereinafter, a portion that strongly reflects thecharacteristics of the cell in the Raman spectrum of the cell isreferred to as a fingerprint region. In the second embodiment, celldetermination is performed using the fingerprint region.

FIG. 13 is a graph showing an example of a fingerprint region in theRaman spectra. The Raman spectra shown in FIG. 13 are the same as theRaman spectra shown in FIG. 4. The range of the fingerprint region is arange in which the Raman shift is 1250 to 1750 cm⁻¹. The fingerprintregion reflects important information derived from components inside thecell, such as the secondary structure of cytochrome C, nucleic acid,lipid, or amide (such as amide I, II, or III). For this reason, thefingerprint region strongly reflects the characteristics of the cell,and changes greatly depending on the cell type as compared with otherportions in the Raman spectrum.

Also in the second embodiment, the configuration of the Raman scatteredlight measuring apparatus 1 is the same as that in the first embodiment.As in the first embodiment, the Raman scattered light measuringapparatus 1 performs the learning of the support vector machine fordetermining the cell type by performing the processing of S11 to S17. InS11, the Raman scattered light measuring apparatus 1 acquires aplurality of Raman spectra by measuring a Raman spectrum one by one fromeach of the plurality of cells contained in the sample 5.

In S13, the CPU 21 of the determination apparatus 2 performs principalcomponent analysis of a fingerprint region in a plurality of Ramanspectra. That is, the CPU 21 performs principal component analysis ofmultidimensional data corresponding to the fingerprint region among aplurality of pieces of multidimensional data representing the pluralityof Raman spectra. For example, in the matrix shown in FIG. 5, a matrixextracted from a portion corresponding to a range in which the Ramanshift is 1250 to 1750 cm⁻¹ is a target of the principal componentanalysis. The CPU 21 performs calculation for generating the spectra ofa plurality of principal components relevant to the fingerprint region,such as a spectrum of a first principal component relevant to thefingerprint region and a spectrum of a second principal componentrelevant to the fingerprint region, by the principal component analysis.

FIGS. 14A and 14B are graphs showing examples of the spectrum of theprincipal component relevant to the fingerprint region. FIGS. 14A and14B show the results of principal component analysis for a plurality ofRaman spectra obtained from a plurality of RBLs and a plurality of CHOs.FIG. 14A shows an example of the spectrum of the first principalcomponent, and FIG. 14B shows an example of the spectrum of the secondprincipal component. The horizontal axis indicates a Raman shift, andthe vertical axis indicates the intensity of Raman scattered light ateach Raman shift.

In S13, the CPU 21 further calculates, for each of the plurality ofRaman spectra, a plurality of principal component scores relevant to thefingerprint region, such as a first principal component score relevantto the fingerprint region and a second principal component scorerelevant to the fingerprint region. The first principal component scorerelevant to the fingerprint region indicates the contribution ratio ofthe fingerprint region in one Raman spectrum to the spectrum of thefirst principal component relevant to the fingerprint region. Aplurality of principal component scores relevant to the fingerprintregion are obtained for each Raman spectrum and cell.

In addition, in S13, the determination apparatus 2 may generate aspectrum of the principal component for the range of the Raman shiftwider than the range of the fingerprint region, extract a portionrelevant to the fingerprint region from the spectrum of the principalcomponent, and calculate a principal component score relevant to thefingerprint region using the extracted portion. In addition, the Ramanscattered light measuring apparatus 1 may extract a fingerprint regionfrom the Raman spectrum measured in S11 and perform the processing ofS12 and subsequent steps on the data representing the extractedfingerprint region. In addition, when measuring the Raman spectrum inS11, the Raman scattered light measuring apparatus 1 may measure onlythe fingerprint region and perform the processing of S12 and subsequentsteps on the data representing the measured fingerprint region.Whichever method is used, a plurality of principal component scoresrelevant to the fingerprint region can be obtained.

Then, in S14, the CPU 21 generates a coordinate space includingcoordinate points having a plurality of principal component scoresrelevant to the fingerprint region as coordinate components. Forexample, the CPU 21 plots, on the two-dimensional coordinates, aplurality of coordinate points having a first principal component scorerelevant to the fingerprint region and a second principal componentscore relevant to the fingerprint region as coordinate components. InS15, the CPU 21 performs the learning of the support vector machineusing the cell information and a plurality of principal component scoresrelevant to the fingerprint region as training data. For example, inorder to determine the cell type, the CPU 21 performs processing fordividing the coordinate space, which includes a plurality of coordinatepoints having a first principal component score relevant to thefingerprint region and a second principal component score relevant tothe fingerprint region as coordinate components, into a plurality ofregions using a support vector machine. As in the first embodiment, theCPU 21 adjusts the parameters of the support vector machine so as todivide the coordinate space so that coordinate points corresponding todifferent types of cells are included in different regions.

FIG. 15 is a characteristic diagram showing an example in which thecoordinate space is divided by the support vector machine according tothe second embodiment. The horizontal axis indicates the first principalcomponent score relevant to the fingerprint region, and the verticalaxis indicates the second principal component score relevant to thefingerprint region. In the diagram, a plurality of coordinate pointshaving a first principal component score relevant to the fingerprintregion and a second principal component score relevant to thefingerprint region as coordinate components are included in thetwo-dimensional coordinate space. FIG. 15 shows a plurality ofcoordinate points corresponding to a plurality of Raman spectra obtainedfrom a plurality of RBLs and shows a plurality of coordinate pointscorresponding to a plurality of Raman spectra obtained from a pluralityof CHOs. From the same type of cells, similar fingerprint regions areobtained, and the principal component scores relevant to the fingerprintregions are similar numerical values. For this reason, in the coordinatespace, coordinate points corresponding to the same type of cells tend tobe located close to each other, and coordinate points corresponding todifferent types of cells tend to be located away from each other. Aplurality of coordinate points surrounded by the broken line correspondto a plurality of CHOs. A plurality of coordinate points surrounded bythe one-dot chain line correspond to a plurality of RBLs. The CPU 21adjusts the parameters of the support vector machine so that thetwo-dimensional coordinate space can be divided into a region includingcoordinate points corresponding to CHOs and a region includingcoordinate points corresponding to RBLs. FIG. 15 shows an example inwhich the boundary 61 of a plurality of divided regions are indicated bypolygonal lines. In addition, the CPU 21 adjusts the parameters of thesupport vector machine so that the margin between the coordinate pointand the boundary 61 in the coordinate space is as large as possible.

The CPU 21 performs the processing of S16 and S17 as in the firstembodiment. As a result of S11 to S17, learned data including dataindicating the spectrum of the principal component relevant to thefingerprint region, the boundary 61, and the type of each cellcorresponding to each region divided by the boundary 61 is stored in thestorage unit 24. As in the first embodiment, the determination apparatus2 may perform processing for acquiring training data or learned datafrom the outside.

As in the first embodiment, the Raman scattered light measuringapparatus 1 performs the processing of S41 to S47 to determine the typeof each cell contained in the sample 5 using the learned data. In S41,the Raman scattered light measuring apparatus 1 acquires one Ramanspectrum for each cell contained in the sample 5. In S43, the CPU 21 ofthe determination apparatus 2 extracts a fingerprint region from theacquired Raman spectrum, and calculates the degree of matching of theextracted fingerprint region with respect to the spectrum of theprincipal component relevant to the fingerprint region represented bythe data included in the learned data. The CPU 21 calculates the degreeof matching relevant to the fingerprint region using the samecalculation method as the calculation method for calculating theprincipal component score. The CPU 21 calculates, for one Ramanspectrum, the same number of degrees of matching relevant to thefingerprint region as the number of principal component scores relevantto the fingerprint region. For example, the CPU 21 calculates a firstdegree of matching, which is calculated by a calculation method similarto that for the first principal component score, and a second degree ofmatching, which is calculated by a calculation method similar to thatfor the second principal component score.

In addition, when measuring the Raman spectrum in S41, the Ramanscattered light measuring apparatus 1 may measure only the fingerprintregion and perform the processing of S42 and subsequent steps on thedata representing the measured fingerprint region. Also in this case,the degree of matching relevant to the fingerprint region can beobtained.

The CPU 21 performs the processing of S44 to S47 as in the firstembodiment. By the processing of S41 to S47, the determination result ofthe type of each cell contained in the sample 5 is obtained. Asdescribed above, in the second embodiment, learning of the supportvector machine and determination of a cell are performed using afingerprint region in the Raman spectrum obtained from the cell. Sincethe fingerprint region strongly reflects the characteristics of the celland greatly changes depending on the type of the cell, the determinationapparatus 2 can cause the support vector machine to perform learning sothat different types of cells can be more reliably classified by usingthe fingerprint region. By using this support vector machine, thedetermination apparatus 2 can more accurately determine the type of eachcell.

In the first and second embodiments described above, a form in which theRaman scattered light measuring apparatus 1 irradiates the sample 5 withlaser light is shown. However, the Raman scattered light measuringapparatus 1 may irradiate the sample 5 with excitation light other thanthe laser light in order to measure the Raman spectrum. In addition, inthe first and second embodiments, a form is shown in which the sample 5is moved to change a portion where Raman scattered light is generated inthe sample 5. However, the Raman scattered light measuring apparatus 1may change the optical path of excitation light in order to change aportion where Raman scattered light is generated in the sample 5.

In addition, in the first and second embodiments, a form is shown inwhich, in order to measure one Raman spectrum from one cell, one entirecell is irradiated with laser light and the Raman spectrum is measured.However, the Raman scattered light measuring apparatus 1 maysequentially irradiate a plurality of portions in one cell withexcitation light, measure a plurality of Raman spectra for the pluralityof portions in the one cell, and create one Raman spectrum representingthe plurality of Raman spectra. For example, the Raman scattered lightmeasuring apparatus 1 creates a Raman spectrum by averaging theplurality of Raman spectra. Also in this case, one Raman spectrum ismeasured from one cell. The Raman scattered light measuring apparatus 1in which an optical system is set so as to sequentially irradiate aplurality of portions in one cell with excitation light can also be usedto determine the cell type.

In addition, in the first and second embodiments, a form is shown inwhich the processing for dividing the two-dimensional coordinate spaceinto a plurality of regions by the support vector machine is performedin order to determine the cell type. However, the determinationapparatus 2 may perform processing for dividing a coordinate space ofthree or more dimensions into a plurality of regions. For example, in aform in which the determination apparatus 2 performs processing fordividing the three-dimensional coordinate space into a plurality ofregions, a coordinate space including coordinate points having a firstprincipal component score, a second principal component score, and athird principal component score as coordinate components and acoordinate space including coordinate points having first, second, andthird degrees of matching as coordinate components are used, and theboundary 61 is a plane or a curved surface. In addition, in the firstand second embodiments, a form is shown in which the support vectormachine is used as a learning model using supervised learning. However,the determination apparatus 2 may use a learning model other than thesupport vector machine. For example, the determination apparatus 2 mayuse a convolutional neural network as a learning model using supervisedlearning. In addition, the determination apparatus 2 can be used notonly for determining the cell type but also for determining a portion ina cell or a biological substance, such as determining the cell nucleustype or the protein type. In addition, in the first and secondembodiments, a form is shown in which the Raman scattered lightmeasuring apparatus 1 and the determination apparatus 2 are integratedto form an analyzer. However, the determination apparatus 2 may beseparated from the Raman scattered light measuring apparatus 1.

The present invention is not limited to the content of theabove-described embodiments, and various changes can be made within thescope of the claims. That is, embodiments obtained by combiningtechnical means appropriately changed within the scope of the claims arealso included in the technical scope of the present invention.

(Note 1)

A method of determining a type of each cell contained in a sample,comprising:

acquiring one Raman spectrum from one undetermined cell;

calculating a plurality of degrees of matching indicating a degree ofmatching of a portion, which corresponds to a predetermined Raman shiftrange of the Raman spectrum of the undetermined cell, with respect to aportion corresponding to the Raman shift range of spectra of a pluralityof principal components obtained by principal component analysis ofportions corresponding to the Raman shift range of a plurality of Ramanspectra that are obtained one by one from each of a plurality of knowntypes of cells; and

determining a type of the undetermined cell by classifying the pluralityof degrees of matching, based on a result obtained by classifying aplurality of principal component scores corresponding to each of theplurality of known types of cells obtained by the principal componentanalysis depending on the type of cells by a learning model usingsupervised learning.

(Note 2)

A method of performing learning for determining a type of each cellcontained in a sample based on a Raman spectrum, comprising:

acquiring a portion corresponding to a predetermined Raman shift rangeof spectra of a plurality of principal components, which are obtained byprincipal component analysis of a portion corresponding to the Ramanshift range of a plurality of Raman spectra that are obtained one by onefrom each of a plurality of known types of cells, and a plurality ofprincipal component scores corresponding to each of the plurality ofcells;

performing machine learning of a learning model so that a plurality ofsets of the plurality of principal component scores can be classifieddepending on the type of cells by the learning model using supervisedlearning with, as training data, the plurality of sets of the pluralityof principal component scores and each of the types of the plurality ofcells; and

storing the portion corresponding to the Raman shift range of spectra ofthe plurality of principal components and a result of classification ofthe plurality of principal component scores by the learning model afterlearning.

(Note 3)

An apparatus for determining a type of each cell contained in a sample,comprising:

a calculation unit that calculates a plurality of degrees of matching ofa portion, which corresponds to a predetermined Raman shift range of aRaman spectrum acquired from an undetermined cell, with respect to aportion corresponding to the Raman shift range of spectra of a pluralityof principal components obtained by principal component analysis ofportions corresponding to the Raman shift range of a plurality of Ramanspectra that are obtained one by one from each of a plurality of knowntypes of cells; and

a determination unit that determines a type of the undetermined cell byclassifying the plurality of degrees of matching, based on a resultobtained by classifying a plurality of principal component scorescorresponding to each of the plurality of known types of cells obtainedby the principal component analysis depending on the type of cells by alearning model using supervised learning.

(Note 4)

A computer program for causing a computer to execute a process fordetermining a type of each cell contained in a sample, the computerprogram causing the computer to execute a process including:

a step of calculating a plurality of degrees of matching indicating adegree of contribution of a portion, which corresponds to apredetermined Raman shift range of a Raman spectrum acquired from anundetermined cell, with respect to a portion corresponding to the Ramanshift range of spectra of a plurality of principal components obtainedby principal component analysis of portions corresponding to the Ramanshift range of a plurality of Raman spectra that are obtained one by onefrom each of a plurality of known types of cells; and

a step of determining a type of the undetermined cell by classifying theplurality of degrees of matching, based on a result obtained byclassifying a plurality of principal component scores corresponding toeach of the plurality of known types of cells obtained by the principalcomponent analysis depending on the type of cells by a learning modelusing supervised learning.

(Note 5)

A computer program for causing a computer to perform learning fordetermining a type of each cell contained in a sample, the computerprogram causing the computer to execute a process including:

a step of acquiring a portion corresponding to a predetermined Ramanshift range of spectra of a plurality of principal components, which areobtained by principal component analysis of a portion corresponding tothe Raman shift range of a plurality of Raman spectra that are obtainedone by one from each of a plurality of known types of cells, and aplurality of principal component scores corresponding to each of theplurality of cells;

a step of performing machine learning of a learning model so that theplurality of principal component scores can be classified depending onthe type of cells by the learning model using supervised learning with,as training data, a plurality of sets of the plurality of principalcomponent scores and each of the types of the plurality of cells; and

a step of storing the portion corresponding to the Raman shift range ofspectra of the plurality of principal components and a result ofclassification of the plurality of principal component scores by thelearning model after learning.

(Note 6)

A learning method of learning for determining a type of each cellcontained in a sample based on a Raman spectrum, comprising:

acquiring spectra of a plurality of principal components, which areobtained by principal component analysis of a plurality of Raman spectrathat are obtained one by one from each of a plurality of known types ofcells, and a plurality of principal component scores corresponding toeach of the plurality of cells;

performing machine learning of a learning model so that a plurality ofsets of the plurality of principal component scores are able to beclassified depending on the type of the plurality of cells by thelearning model using supervised learning with, as training data, theplurality of sets of the plurality of principal component scores andeach of the types of the plurality of cells; and

storing the spectra of the plurality of principal components and aresult of classification of the plurality of principal component scoresby the learning model after learning.

(Note 7)

The learning method according to Note 6, wherein

the plurality of sets of the plurality of principal component scores areclassified by dividing a coordinate space, in which a plurality ofcoordinate points having the plurality of principal component scores ascoordinate components are included, into a plurality of regions by thelearning model.

(Note 8)

A recording medium recording a computer program for causing a computerto perform learning for determining a type of each cell contained in asample, the computer program causing the computer to execute a processincluding:

a step of acquiring spectra of a plurality of principal components,which are obtained by principal component analysis of a plurality ofRaman spectra that are obtained one by one from each of a plurality ofknown types of cells, and a plurality of principal component scorescorresponding to each of the plurality of cells;

a step of performing machine learning of a learning model so that theplurality of principal component scores are able to be classifieddepending on the type of the plurality of cells by the learning modelusing supervised learning with, as training data, a plurality of sets ofthe plurality of principal component scores and each of the types of theplurality of cells; and

a step of storing the spectra of the plurality of principal componentsand a result of classification of the plurality of principal componentscores by the learning model after learning.

It is to be noted that, as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural referents unless thecontext clearly dictates otherwise.

It is to be noted that the disclosed embodiment is illustrative and notrestrictive in all aspects. The scope of the present invention isdefined by the appended claims rather than by the description precedingthem, and all changes that fall within metes and bounds of the claims,or equivalence of such metes and bounds thereof are therefore intendedto be embraced by the claims.

The invention claimed is:
 1. A determination method of determining atype of each cell contained in a sample, comprising: acquiring one Ramanspectrum from one undetermined cell; calculating a plurality of degreesof matching of a Raman spectrum of the undetermined cell with respect tospectra of a plurality of principal components obtained by principalcomponent analysis of a plurality of Raman spectra that are obtained oneby one from each of a plurality of known types of cells; and determininga type of the undetermined cell by classifying the plurality of degreesof matching, based on a result obtained by classifying a plurality ofprincipal component scores corresponding to each of the plurality ofknown types of cells obtained by the principal component analysisdepending on the type of cells by a learning model using supervisedlearning.
 2. The determination method according to claim 1, wherein thelearning model is a support vector machine.
 3. The determination methodaccording to claim 1, wherein the machine learning of the learning modelis performed using, as training data, the plurality of principalcomponent scores corresponding to each of the plurality of known typesof cells and each of the types of the plurality of cells.
 4. Thedetermination method according to claim 1, wherein one entire cell isirradiated with excitation light, and a Raman spectrum is acquired bymeasuring Raman scattered light from the one entire cell.
 5. Adetermination apparatus for determining a type of each cell contained ina sample, comprising: a processor; and a memory, wherein the processoris operable to: calculate a plurality of degrees of matching of a Ramanspectrum acquired from an undetermined cell with respect to spectra of aplurality of principal components obtained by principal componentanalysis of a plurality of Raman spectra that are obtained one by onefrom each of a plurality of known types of cells; and determine a typeof the undetermined cell by classifying the plurality of degrees ofmatching based on a result obtained by classifying a plurality ofprincipal component scores corresponding to each of the plurality ofknown types of cells obtained by the principal component analysisdepending on the type of cells by a learning model using supervisedlearning.
 6. The determination apparatus according to claim 5, whereinthe processor is further operable to perform machine learning of thelearning model using, as training data, the plurality of principalcomponent scores corresponding to each of the plurality of known typesof cells and each of the types of the plurality of cells.
 7. Thedetermination apparatus according to claim 6, wherein the processor isfurther operable to acquire the training data from outside.
 8. Thedetermination apparatus according to claim 5, wherein the processor isfurther operable to acquire, from outside, the spectra of the pluralityof principal components and a result, which is obtained by classifyingthe plurality of principal component scores corresponding to each of theplurality of known types of cells depending on the type of cells by thelearning model.
 9. A recording medium recording a computer program forcausing a computer to execute a process for determining a type of eachcell contained in a sample, the computer program causing the computer toexecute a process including: a step of calculating a plurality ofdegrees of matching indicating a degree of contribution of a Ramanspectrum acquired from an undetermined cell to spectra of a plurality ofprincipal components obtained by principal component analysis of aplurality of Raman spectra that are obtained one by one from each of aplurality of known types of cells; and a step of determining a type ofthe undetermined cell by classifying the plurality of degrees ofmatching, based on a result obtained by classifying a plurality ofprincipal component scores corresponding to each of the plurality ofknown types of cells obtained by the principal component analysisdepending on the type of cells by a learning model using supervisedlearning.